Bayesian Models for Detecting Selection in Malaria Genes

用于检测疟疾基因选择的贝叶斯模型

• 批准号: 6879499
• 负责人: Raquel Prado
• 金额: $ 10.11万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-04-01 至 2009-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6879499
• 关键词: Plasmodium; Plasmodium falciparum; binding sites; biochemical evolution; computer simulation; geographic site; high throughput technology; malaria; mathematical model; method development; model design /development; molecular biology information system; molecular dynamics; molecular genetics; nucleic acid quantitation /detection; nucleic acid sequence; protein structure function; protozoal antigen; protozoal genetics; statistics /biometry

DESCRIPTION (provided by applicant): The proposed research considers the development of new statistical methods to detect positive natural selection in genes encoding malaria antigens. Our research is motivated by the analysis of multiple DNA sequences encoding the Apical Membrane Antigen 1 (AMA-i), the Circumsporozoite Protein (CSP) and the Merozoite Surface Protein (MSP-1) in the P.falciparum and P.vivax human malaria parasites. Several DNA sequences collected in different geographical areas in Africa, Asia and South America are available for each antigen. The fact that a large number of very similar sequences is available implies that a single phylogenetic structure cannot be assumed, as very many phylogenies are equally likely to explain the evolution of these sequences. The development of new statistical methods based on a class of hierarchical generalized linear models is proposed. These methods do not require the specification of a phylogenetic structure and can predict sites under positive selection in a relatively fast way. The new approach is Bayesian and will need the implementation of efficient computational methods for parameter estimation. The new models allow for the incorporation of information that might be relevant to infer the pattern of substitutions, such as geographical location and information on pairwise evolutionary distances if available. The specific goals of the proposed research are: (1) developing new statistical models to detect molecular adaptation in a large number of DNA protein coding sequences that are closely related in evolutionary time, and for which little or no phylogenetic structure is available, (2) assessing the predictive performance of the new models via simulation studies for different kinds of datasets and comparing the new and current methodologies to address the problem of identifying sites under positive selection in DNA sequences, (3) analyzing a large number of DNA sequences encoding malaria antigens using the new models.

描述（由申请人提供）：拟议的研究考虑了开发新的统计方法，以检测编码疟疾抗原的基因中阳性自然选择。我们的研究是由编码编码顶层膜抗原1（AMA-I），割孢子岩蛋白（CSP）和Merozoite表面蛋白（MSP-1）的多个DNA序列的动机。每种抗原都可以使用在非洲，亚洲和南美不同地理区域收集的几个DNA序列。大量非常相似的序列可用的事实意味着不能假设单个系统发育结构，因为许多系统发育同样有可能解释这些序列的演变。提出了基于一类层次通用线性模型的新统计方法的开发。这些方法不需要系统发育结构的规范，并且可以以相对较快的方式预测位置。新方法是贝叶斯，需要实施有效的计算方法来进行参数估计。新模型允许合并可能与推断替换模式相关的信息，例如地理位置的位置以及有关成对进化距离的信息。 拟议的研究的具体目标是：（1）开发新的统计模型，以在大量的DNA蛋白质编码序列中检测分子适应，这些序列在进化时间密切相关，并且很少或否可用或否可用的系统发育结构，（2）通过对新方法进行模拟研究，以评估新模型的预测性能，以确定新的方法，以确定不同的方法，该方法的方法是不同的，该方法的方法是不同的。 （3）分析使用新模型编码疟疾抗原的大量DNA序列。